The dataset used for this analysis comes from AirBnB listings and is rich with information pertinent to understanding the rental landscape.
This introduction to the dataset sets the stage for a detailed exploration of the factors that influence the AirBnB market in Paris through a user-friendly Shiny application.
The Shiny application is designed to convert complex dataset analyses into accessible insights with an intuitive interface. It serves the following purposes:
The dataset was initially sourced from an .Rdata file. Preprocessing involved several steps to ensure the data was clean and analysis-ready.
The dataset was loaded into R for analysis. We carefully reviewed the variables to gauge their relevance and utility for our objectives.
load("C:/Users/NADER/Downloads/AirBnB (1) (1).Rdata")Before diving into the analysis and visualization, it’s crucial to clean and transform our dataset. This section outlines the steps taken to ensure our data is analysis-ready.
The dataset contains prices in a text format, including currency symbols and commas. For accurate analysis, we convert these text strings into numeric values by removing non-numeric characters.
L$price <- as.numeric(gsub("[$,]", "", L$price))The amenities and description fields are essential for our analysis. To facilitate string operations later on, we ensure they are treated as character vectors.
L$amenities <- as.character(L$amenities)description needs similar treatmentL$description <- as.character(L$description)Each property listing includes amenities, which are provided as a concatenated string. We parse these strings to count the number of amenities for each listing, providing a more quantitative measure for analysis.
L$number_amenities <- sapply(strsplit(L$amenities, "[{}\",]"), function(x) length(x[x != ""]))Some listings may not specify their amenities, resulting in NA values for the number_amenities field. To maintain data integrity, we assume these listings have zero amenities.
L$number_amenities[is.na(L$number_amenities)] <- 0For any time series analysis or visualizations that involve dates, it’s essential to ensure the date data is in the correct format. Here, we convert the date field from a character to a Date object.
R$date <- as.Date(as.character(R$date))With these preparation steps complete, our dataset is now ready for exploratory data analysis (EDA), modeling, and visualization within our Shiny application.
This section succinctly documents the essential data preparation steps, making it clear what transformations are performed and why. Including this in your R Markdown document ensures that your analytical process is transparent and reproducible.
The user interface (UI) of our Shiny application is designed to be intuitive and user-friendly, facilitating easy navigation through the various analyses available. Here’s an overview of the UI components and their functionalities:
The application utilizes the shinydashboard package to
create a polished and responsive dashboard. The dashboard is divided
into three main sections: the header, sidebar, and body.
The dashboard header is defined with a title for the application.
dashboardHeader(title = "AirBnB Analysis", titleWidth = 250)dashboardSidebar(
width = 250,
sidebarMenu(
menuItem("Price vs Features", tabName = "price_features", icon = icon("chart-line")),
menuItem("Apartments per Owner", tabName = "apartments_owner", icon = icon("building")),
menuItem("Price per Quarter", tabName = "price_quarter", icon = icon("calendar-alt")),
menuItem("Visit Frequency", tabName = "visit_frequency", icon = icon("clock")),
menuItem("Interactive Map", tabName = "interactive_map", icon = icon("map-marked-alt"))
)
)Styling Custom CSS is applied to personalize the appearance of the dashboard, enhancing the visual appeal and user experience.
tags$head(
tags$style(HTML("
.content-wrapper { background-color: #FFF0F0 !important; }
.main-header .navbar, .main-header .logo { background-color: #FF5A5F !important; }
.main-sidebar { background-color: #D50000 !important; }
.sidebar-menu > li > a { color: #FFFFFF !important; }
.sidebar-menu > li:hover > a, .sidebar-menu > li.active > a { background-color: #FF5A5F !important; color: #FFFFFF !important; }
.sidebar-menu .header { color: #FFD3D3 !important; }
.box-body { background-color: #FFEBEB !important; }
"))
)Dynamic Content Each tabItem contains dynamic content, such as input widgets for selecting features to analyze and output plots that display the results of the analysis.
Dynamic Correlation Analysis: Users can select features to explore their relationship with property prices. Distribution of Apartments per Owner: Offers insights into the number of properties managed by individual hosts. Average Renting Price per City Quarter: Visualizes the variation in rental prices across different city quarters. Visit Frequency of Different Quarters Over Time: Analyzes the popularity of city quarters based on visitation data. Interactive Map: Displays AirBnB listings on an interactive map, providing a geographical perspective on the data.
The server logic of our Shiny application is where the reactive elements and outputs are defined. It processes user inputs and computes the outputs that are rendered in the UI. Let’s go through the main components of the server logic:
The scatter plot dynamically visualizes the relationship between the listing price and a feature selected by the user.
#output$scatterPlot <- renderPlotly({
# # Generate scatter plot based on selected feature
# feature_selected <- input$featureSelect
# plot_data <- L %>% select(price, !!sym(feature_selected)) %>% na.omit()
# p <- ggplot(plot_data, aes_string(x = feature_selected, y = "price")) +
# geom_point(aes(color = price), alpha = 0.5) +
# labs(x = feature_selected, y = "Price in Euros") +
# theme_minimal()
# ggplotly(p)
#})This block listens for changes to input$featureSelect, extracts the relevant data, and plots it using ggplot2 and plotly for interactive visualization.
Apartments Per Owner Analysis This part computes the distribution of apartments across owners, allowing for insights into property ownership concentration.
apartments_per_owner <- reactive({
data <- L %>%
group_by(host_id, host_name) %>%
summarise(number_of_apartments = n_distinct(id), .groups = 'drop') %>%
arrange(desc(number_of_apartments))
data <- data %>%
filter(number_of_apartments >= input$apartmentFilter[1] &
number_of_apartments <= input$apartmentFilter[2])
data
})
#output$ownerTable <- renderDataTable({
# apartments_per_owner()
#}, options = list(pageLength = 10))This section creates a reactive expression to filter and summarize the data based on user input and then renders it as a datatable.
Average Renting Price Per City Quarter Calculates and visualizes the average renting price for selected city quarters.
# output$avgPricePlot <- renderPlotly({
# filtered_data <- if (length(input$quarterSelect) > 0) {
# L %>% filter(neighbourhood_cleansed %in% input$quarterSelect)
# } else {
# L
# }
# avg_prices <- filtered_data %>%
# group_by(neighbourhood_cleansed) %>%
# summarise(Average_Price = mean(price, na.rm = TRUE)) %>%
# arrange(desc(Average_Price))
# p <- ggplot(avg_prices, aes(x = reorder(neighbourhood_cleansed, Average_Price), y = Average_Price)) +
# geom_bar(stat = "identity", fill = "lightpink") +
# labs(title = "Average Renting Price per City Quarter", x = "City Quarter", y = "Average Price (€)") +
# theme_minimal() +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# ggplotly(p)
# }This output visualizes the average renting prices across different city quarters based on user selections.
Visit Frequency Over Time Analyzes and plots the visit frequency to different city quarters over a selected time range.
#output$visitFrequencyPlot <- renderPlotly({
# quarters_data <- if (length(input$quarterFrequencySelect) > 0) {
# L %>% filter(neighbourhood_cleansed %in% input$quarterFrequencySelect)
# } else {
# L
# }
# merged_data <- merge(quarters_data, R, by.x = "id", by.y = "listing_id")
# date_filtered_data <- merged_data %>%
# filter(date >= input$dateRange[1] & date <= input$dateRange[2])
# visit_frequency_data <- date_filtered_data %>%
# group_by(neighbourhood_cleansed, month = floor_date(date, "month")) %>%
# summarise(Visits = n()) %>%
# arrange(neighbourhood_cleansed, month)
# p <- ggplot(visit_frequency_data, aes(x = month, y = Visits, color = #neighbourhood_cleansed, group = neighbourhood_cleansed)) +
# geom_line() + geom_point() +
# labs(title = "Visit Frequency Over Time by City Quarter", x = "Month", y = #"Number of Visits") +
# theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
# ggplotly(p)
#})This code block creates an interactive time-series plot showing how visitation to different quarters changes over time.
Interactive Map of Airbnb Listings Renders an interactive map displaying Airbnb listings with detailed information.
#output$airbnbMap <- renderLeaflet({
# data <- L %>% filter(!is.na(latitude) & !is.na(longitude)) %>%
# mutate(price = as.numeric(gsub("[$,]", "", price)))
# leaflet(data) %>%
# addTiles() %>%
# addMarkers(~longitude, ~latitude, popup = ~paste("Price: $", price, "<br>", #"Neighbourhood: ", neighbourhood_cleansed, "<br>", "Accommodates: ", #accommodates), clusterOptions = markerClusterOptions())
#})This segment of the server logic uses the leaflet package to create a map visualization of the listings, enhancing the geographic analysis of the data. shinyApp(ui, server)
The statistical results derived from the Shiny app provide insightful observations about the AirBnB listings:
Overall, the data underscores the multifaceted nature of the rental market and highlights the importance of a range of factors from amenities and capacity to location and seasonality in influencing prices and popularity of listings.
Through the interactive visualizations and analyses provided by our Shiny app, several key insights into the AirBnB market have been uncovered:
Amenities Impact on Price: The presence of amenities contributes to higher listing prices, but the relationship is complex and influenced by other factors such as location and unique property features.
Property Size and Price Correlation: There is a general trend that larger properties (more bedrooms, bathrooms, and higher accommodation capacity) command higher prices. However, outliers suggest that premium pricing is also attributed to exceptional properties or desirable locations.
Host Property Portfolios: A small number of hosts control a significant proportion of the listings, indicating a possible trend towards professional hosting or investment properties in the short-term rental market.
Geographical Pricing Trends: Location remains a prime factor in pricing strategies, with central and popular city quarters having higher average rental prices. This underlines the classic real estate adage of “location, location, location”.
Seasonal and Temporal Popularity: The visitation patterns reflect seasonal trends and suggest that city quarters rise and fall in popularity over time. This dynamic may be influenced by a variety of factors, including seasonal events, changes in urban infrastructure, and evolving traveler preferences.
Data-Driven Decisions for Hosts and Renters: Both hosts and renters can leverage these insights for better decision-making. Hosts can optimize their pricing and amenities based on what’s valued in the market, while renters can make informed choices about when and where to book properties.
Market Complexity and Opportunities: The AirBnB market in Paris is multifaceted, offering opportunities for hosts to differentiate their listings and for renters to find properties that best match their preferences.
The Shiny application facilitates a deeper understanding of the nuanced AirBnB rental landscape, and the interplay of factors affecting it. This analysis can help stakeholders to navigate the market more effectively and underscores the value of data-driven approaches in the sharing economy.